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Unit of study_

DATA2901: Big Data and Data Diversity (Advanced)

This course focuses on methods and techniques to efficiently explore and analyse large data collections. Where are hot spots of pedestrian accidents across a city? What are the most popular travel locations according to user postings on a travel website? The ability to combine and analyse data from various sources and from databases is essential for informed decision making in both research and industry. Students will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects, such as relational, semi-structured, time series, geospatial, image, text. As well as reinforcing their programming skills through experience with relevant Python libraries, this course will also introduce students to the concept of declarative data processing with SQL, and to analyse data in relational databases. Students will be given data sets from, eg. , social media, transport, health and social sciences, and be taught basic explorative data analysis and mining techniques in the context of small use cases. The course will further give students an understanding of the challenges involved with analysing large data volumes, such as the idea to partition and distribute data and computation among multiple computers for processing of 'Big Data'. This unit is an alternative to DATA2001, providing coverage of some additional, more sophisticated topics, suited for students with high academic achievement.


Academic unit Computer Science
Unit code DATA2901
Unit name Big Data and Data Diversity (Advanced)
Session, year
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

75% or above from (DATA1002 OR DATA1902 OR INFO1110 OR INFO1903 OR INFO1103)
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Uwe Roehm,
Lecturer(s) Uwe Roehm ,
Type Description Weight Due Length
Final exam Final examination
Written final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4
Small test SQL quiz
SQL quiz (in-lab) at start of tutorials of Week 8
20% Week 08 1 hour
Outcomes assessed: LO3 LO4 LO8
Assignment group assignment Assignment
Practical data integration and data analysis assignment.
20% Week 12 ca 4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO8
group assignment = group assignment ?
  • SQL: Students work through weekly online tutorials introducing increasingly sophisticated usage of SQL. The SQL tutorials provide simple feedback and allow multiple attempts, and example solutions are available after the submission deadline has passed. Solutions are provided for each week, and the topics are assessed in an SQL quiz.
  • Practical Assignment: Students work in teams on a larger data integration and data analysis task, where some supplied datasets have to be combined with additional data researched by students. The final submission consists of the source code artifacts developed by the teams, plus a short report of their findings, and a group demo during the labs of Week 12.
  • Final exam: Understanding of all of this unit’s material is reviewed in a written examination.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late submission penalty for the practical assignment: -20% of the awarded marks per day late; minimum 0% after 5 days.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

WK Topic Learning activity Learning outcomes
Multiple weeks SQL self-study tutorials via Grok Learning online platform Independent study (16 hr) LO2 LO4
Project Work (practical assignment) - own time Independent study (16 hr) LO1 LO2 LO3 LO6 LO7
Ongoing revision of weekly material and working on weekly tutorial exercises (homework) Independent study (40 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Week 01 Introduction and Motivation: What is big data? Challenges for data analytics Lecture and tutorial (4 hr) LO1 LO2 LO3 LO5
Advanced topic: Data Analysis with Unix Seminar (1 hr) LO8
Week 02 Data Analysis with Python / Jupyter Notebooks Lecture and tutorial (4 hr) LO1 LO2 LO3
Advanced topic: Unix and Jupyter magics Seminar (1 hr) LO8
Week 03 Accessing data in databases; introduction to SQL Lecture and tutorial (4 hr) LO2 LO3 LO4
Advanced topic: Descriptive Statistics with SQL / ipython-SQL Seminar (1 hr) LO4 LO8
Week 04 Declarative Data Analysis with SQL Lecture and tutorial (4 hr) LO3 LO4 LO5
Advanced topic: Trend Analysis with SQL & SQL over external data Seminar (1 hr) LO4 LO8
Week 05 Scalable Data Analytics: the role of indexes and data partitioning Lecture and tutorial (4 hr) LO4 LO5 LO6
Advanced topic: How to get computation close to data? A look at MADlib. Seminar (1 hr) LO6 LO8
Week 06 Web Scraping: Reading and interpreting data from the web Lecture and tutorial (4 hr) LO1 LO2 LO3 LO7
Advanced topic: Web Scraping with Unix Shell Seminar (1 hr) LO2 LO8
Week 07 Web APIs and NoSQL: Working with semi-structured data Lecture and tutorial (4 hr) LO1 LO2 LO3
Advanced topic: Web API Debugging; Scrapy Seminar (1 hr) LO1 LO2 LO3 LO8
Week 08 Text Data Processing: feature extraction and analysis Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Advanced topic: Text Classification with sciKit-learn and SpaCy Seminar (1 hr) LO1 LO2 LO3 LO8
Week 09 Geo-Spatial Data Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Advanced topic: Introduction to Spark Seminar (1 hr) LO5 LO6 LO8
Week 10 Analysing Time Series Data Lecture and tutorial (4 hr) LO1 LO2 LO3
Advanced topic: More on Spark Seminar (1 hr) LO5 LO6 LO8
Week 11 Image Data Processing: feature extraction and analysis Lecture and tutorial (4 hr) LO1 LO2 LO3
Advanced topic: Evaluation of Spark Seminar (1 hr) LO5 LO6 LO8
Week 12 Challenges in analysing Big Data: the What and Why of Hadoop; 2. data privacy/anonymising data Lecture and tutorial (4 hr) LO5 LO6 LO7
Advanced topic: differential privacy Seminar (1 hr) LO7 LO8
Week 13 Revision Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8

Attendance and class requirements

Students are expected to follow the weekly lectures and the additional advanced seminar either in-class or using the lecture recordings, and to work through the weekly tutorial material. During the first eight weeks of the unit, there is also an online SQL tutorial available which students are expected to work through on their own time in order to prepare for the mid-semester SQL quiz.

The practical assignment is group work where all team members are expected to actively participate and to divide the work fairly among the team members. The individual mark awarded for the group assignment is conditional on a team member being able to explain any part of the group submission to the tutor or the lecturer if asked. In particular, groups will have to demo their submissions in the tutorials of Week 12, and based on this demo, the group’s assignment mark will be scaled for each team member based on the individual level of contribution. Further details of this participation scaling will be defined on the assignment handout.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. use appropriate Python libraries to automate data science activities on diverse kinds of data
  • LO2. ingest, combine and summarise data from a variety of data models
  • LO3. demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text, including experience to combine data of different types
  • LO4. understand and produce declarative queries to extract appropriate information from data sets, including competence in use of SQL
  • LO5. understand the main challenges analysing 'big data': data volume, variety, velocity, veracity
  • LO6. understand the impact of data volume on data processing, and awareness of approaches to address this such as indexing, compression, data partitioning, and distributed processing frameworks (Hadoop)
  • LO7. demonstrate awareness of privacy issues when working with data
  • LO8. know and work with several sophisticated topics related to data scale and diversity.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
advanced seminars better aligned with normal lectures; increased time spend on Spark

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Computer programming assignments may also be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see . These programs work in a similar way to TII in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.